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Predicting the popularity of scientific publications by an age-based diffusion model

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Listed:
  • Zhou, Yanbo
  • Li, Qu
  • Yang, Xuhua
  • Cheng, Hongbing

Abstract

Predicting the popularity of scientific publications has attracted much attention from various disciplines. In this paper, we focus on the popularity prediction problem of scientific papers, and propose an age-based diffusion (AD) model to identify papers that will receive more citations and be popular in the near future. The AD model mimics the attention diffusion process along the citation networks. An experimental study shows that the AD model can achieve better prediction accuracy than other benchmark methods. For some newly published papers that have not accumulated many citations but will be popular in the near future, the AD model can substantially improve their rankings. This improvement is critical, because identifying future highly cited papers from large numbers of new papers published each month would provide very valuable references for researchers.

Suggested Citation

  • Zhou, Yanbo & Li, Qu & Yang, Xuhua & Cheng, Hongbing, 2021. "Predicting the popularity of scientific publications by an age-based diffusion model," Journal of Informetrics, Elsevier, vol. 15(4).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:4:s1751157721000481
    DOI: 10.1016/j.joi.2021.101177
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    References listed on IDEAS

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    Cited by:

    1. Yanbo Zhou & Xin-Li Xu & Xu-Hua Yang & Qu Li, 2022. "The influence of disruption on evaluating the scientific significance of papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5931-5945, October.
    2. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.

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